Linkage disequilibrium (LD, the non-random association of alleles at two or more loci) provides valuable information for detecting genetic variations that are responsible for complex human diseases such as hypertension, diabetes, obesity, and stroke. Haplotypes, the combinations of alleles on the same chromosome that were inherited as a unit, may offer valuable insights on the LD structure of the human genome and may provide additional power for mapping disease genes. Such insights may be useful not only in disease gene mapping, but also in other fields such as population genetics, where haplotype information has been used to study migration and immigration rates, genetic demography, and human evolutionary history. The international HapMap project, which aims to develop a haplotype map of the human genome, has already begun to provide valuable resources that can in turn motivate the development and testing of new haplotype methods. Although haplotype analysis using a large quantity of single nucleotide polymorphisms (SNPs) is in great need, it also poses great challenges. The overall goal of this project is to develop novel statistical and computational methods and software tools for the analysis of hapltoypes in mapping of complex human disease genes. The specific objectives of this project are: (1) to develop efficient algorithms to estimate haplotype frequencies and determine individual haplotype configurations in the presence of informatively missing genotypes and genotyping errors in samples of unrelated individuals; (2) to develop statistical methods to identify a set of candidate genomic regions for use in disease association mapping; (3) to develop new haplotype-based disease gene mapping methods that can handle informatively missing genotypes and genotyping errors, that can combine information from multiple regions of interest, and that are robust to population heterogeneity; and (4) to release robust and user-friendly software, which implements the proposed methods, to the scientific community at no charge. The proposed methods will be performed on the publicly available data (e.g. data from the HapMap project), as well as other human data generated in our collaborators' ongoing projects, including data sets concerning genetic effects on left ventricular hypertrophy, rheumatoid arthritis, and obesity. The proposed project is closely related to NIH's mission in that the accomplished methods will be useful to the broad biomedical research community and will greatly facilitate the study of human genetic variation and its association with complex diseases. This will help in pursuit of new knowledge about these diseases. The proposed methods are expected to aid the discovery of the genes that are responsible for complex human diseases, help us to better understand them, and finally enhance our ability to prevent, diagnose, and treat these diseases.